class_docstr: Interpolated version of Kneser-Ney smoothing.
code: def __init__(self, order, discount=0.1, **kwargs):
      if not (0 <= discount <= 1):
          raise ValueError(
              "Discount must be between 0 and 1 for probabilities to sum to unity."
          )
      super().__init__(
          KneserNey,
          order,
          params={"discount": discount, "order": order},
          **kwargs,
      )
  
imports: from nltk.lm.api import LanguageModel, Smoothing
  from nltk.lm.smoothing import AbsoluteDiscounting, KneserNey, WittenBell
label_desc: Use image node_id 1 to create a new KneserNeyInterpolated object from inherited base classes: InterpolatedLanguageModel with example: obj = KneserNeyInterpolated(order, discount)